Faculty of Sciences, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Environ Monit Assess. 2013 Jan;185(1):473-83. doi: 10.1007/s10661-012-2568-2. Epub 2012 Mar 8.
The toxic substances, pesticides, and organic contaminants in effluents can potentially be causing damage that includes increased cancer risk; liver, kidney, stomach, nervous system, and immune system problems; reproductive difficulties; cataracts; and anemia. A quantitative structure-retention relationship (QSRR) was developed using the partial least square (PLS), kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data which contained retention time (RT) of the 47 hazardous compounds in effluents were obtained by reverse-phase high-performance liquid chromatography. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-PLS descriptors are selected for L-M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The described model does not require experimental parameters and potentially provides useful prediction for RT of new compounds. This is the first research on the QSRR of hazardous compounds in effluents using the chemometrics models.
废水中的有毒物质、农药和有机污染物可能会造成损害，包括增加癌症风险；肝脏、肾脏、胃、神经系统和免疫系统问题；生殖困难；白内障；和贫血。使用偏最小二乘 (PLS)、核偏最小二乘 (KPLS) 和 Levenberg-Marquardt 人工神经网络 (L-M ANN) 方法，开发了一种定量结构-保留关系 (QSRR)。通过反相高效液相色谱法获得了废水中 47 种有害化合物的保留时间 (RT) 数据。遗传算法被用作 PLS 和 KPLS 建模方法的因素选择程序。通过比较结果，选择 GA-PLS 描述符用于 L-M ANN。最后，通过 L-M ANN 获得了一个具有低预测误差和良好相关系数的模型。该模型不需要实验参数，并且有可能为新化合物的 RT 提供有用的预测。这是使用化学计量学模型对废水中有害化合物进行 QSRR 的首次研究。
